This paper describes the results of surveys and interviews from over 1800 students in five large STEM classes at a research university when classes abruptly moved online for spring quarter 2020 due to the COVID-19 pandemic. We examine how students’ expectations compared to their realities at the end of the quarter; what factors impacted their spring 2020 quarter; and students’ academic sense of belonging, self-efficacy, cost of engagement, and effort regulation in spring 2020. We contextualize students’ experiences of emergency distance learning (EDL) through quantitative comparisons to previous quarters, open survey responses, and interviews. We also examine heterogeneity with respect to race/ethnicity, gender, first-generation college students, and students from low-income families. We find that there are reasons to expect increased achievement gaps post-EDL, but we also find examples of resiliency and improved self-regulated learning that will be life-long assets for students. Our goal in this paper is to use exploratory findings from one particular context to help identify potential ways to disrupt the reproduction and deepening of educational inequality exacerbated by the crisis, and educational opportunities in these unconventional times. © 2022 Taylor & Francis Group, LLC.

The importance of online learning in higher education settings is growing, not only in wake of the Covid-19 pandemic. Therefore, metrics to evaluate and increase the quality of online instruction are crucial for improving student learning. Whereas instructional quality is traditionally evaluated with course observations or student evaluations, course syllabi offer a novel approach to predict course quality even prior to the first day of classes. This study develops an online course design characteristics rubric for science course syllabi. Utilizing content analysis, inductive coding, and deductive coding, we established four broad high-quality course design categories: course organization, course objectives and alignment, interpersonal interactions, and technology. Additionally, this study exploratively applied the rubric on 11 online course syllabi (N = 635 students) and found that these design categories explained variation in student performance.

A growing body of work has shown that two specific study strategies help explain differences in learning and achievement in gateway courses: spacing (breaking up study sessions across multiple days) and self-testing (actively recalling information from memory). However, it is still unclear whether the benefits of these strategies are applicable in more advanced biology courses, and whether promoting effective study practices in these courses (spacing and self-testing) is related to increased use of these practices and greater learning outcomes. We studied two senior-level microbiology courses that were taught by the same instructor. Using a quasi-experimental design, one course additionally received a light-touch study skills intervention, where the instructor introduced the concepts of spacing and self-testing while also providing reminders to students about utilizing these strategies. We found that, while the intervention was not related to increased use of spacing and self-testing, both strategies were positively related to learning, as measured by the final course grade. Results from multiple regression analyses revealed that engaging in spacing throughout the course was the most consistent predictor of final course grade, even after accounting for other study strategies, demographic characteristics, and prior academic achievement. Our results add to the literature emphasizing the importance of spacing in increasing students’ achievement in STEM courses. © Springer Nature Switzerland AG 2021.

This quantitative study examined student participation in an introductory project-based engineering course offered in fully face-to-face and hybrid course modes (N = 160). This course attempted to counteract trends of decreased student motivation and high attrition rates among engineering majors. Mixed-design analysis of variance examined differences in motivational constructs including student self-efficacy, effort regulation, and interest in engineering, as well as engineering skills throughout the course and across instructional modes. None of the motivational constructs were associated with significant decreases throughout the course nor with differences across instructional modes. However, students’ engineering skills increased throughout the course with no significant differences across course modalities. Furthermore, interest in engineering and effort regulation were positively associated with course performance. The instructional modality was not significantly associated with course performance. Overall, this study provides an example of a project-based introductory engineering course which may help maintain student motivation and foster student success in engineering. © The Author(s) 2021.

Although online courses can provide students with a high-quality and flexible learning experience, one of the caveats is that they require high levels of self-regulation. This added hurdle may have negative consequences for first-generation college students. In order to better understand and support students' self-regulated learning, we examined a fully online Chemistry course with high enrollment (N = 312) and a high percentage of first-generation college students (65.70%). Using students' lecture video clickstream data, we created two indicators of self-regulated learning: lecture video completion and time management. Performing a k-means clustering on these indicators uncovered four distinct self-regulated learning patterns: (1) Early Planning, (2) Planning, (3) Procrastination, and (4) Low Engagement. Early Planning behaviors were especially important for course success-they consistently predicted higher final course grades, even after controlling for important demographic variables. Interestingly, first-generation college students classified as Early Planners achieved at similar levels as their non-first-generation peers, but first-generation students in the Low Engagement group had the lowest average grades among students. Overall, our results show that self-regulation may be an important skill for determining first-generation students' STEM achievement, and targeting these skills may serve as a useful way to support their specific learning needs.

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. * Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (AutoML) are gaining traction. * Other aspects are harder to automate, not only because of technological challenges, but because open-ended and context-dependent tasks require human interaction.

We demonstrate how motivational and behavioral processes can explain which students may be more likely to select into online (OL) than face-to-face (F2F) courses and also less likely to perform well in OL courses. Uni-versity students (n = 999) reported their reasons for OL course selection: university constraints, specific need for flexibility, general preference for flexibility, and learning preferences. Compared to F2F students, only OL stu-dents with certain self-selection reasons showed differences in motivation, behavior, and performance. Notably, OL students who said they had a specific need for flexibility created by the costs of competing responsibilities spent more time on non-academic activities (e.g., working, commuting), less time on academic activities (e.g., study groups), and ultimately performed worse when compared to F2F peers. These students were especially likely to be women, older, and part-time. We discuss implications for practice and for using demographic characteristics to control for selection effects.

Over the last decade, psychological interventions, such as the values affirmation intervention, have been shown to alleviate the male-female performance difference when delivered in the classroom, however, attempts to scale the intervention are less successful. This study provides unique evidence on this issue by reporting the observed differences between two randomized controlled implementations of the values affirmation intervention: (a) successful in-class and (b) unsuccessful online implementation at scale. Specifically, we use natural language processing to explore the discourse features that characterize successful female students’ values affirmation essays to gain insight on the underlying mechanisms that contribute to the beneficial effects of the intervention. Our results revealed that linguistic dimensions related to aspects of cohesion, affective, cognitive, temporal, and social orientation, independently distinguished between males and females, as well as more and less effective essays. We discuss implications for the pipeline from theory to practice and for psychological interventions. © The Author(s) 2021.

Collaborative tasks do not always promote equal learning. Varying levels of social interactions and regulation at the individual and group levels can influence knowledge construction efforts and learning success. To understand which collaboration patterns may be more conducive to learning, this study examined the relation between social exchange, regulation, and learning outcomes. Four project-based engineering undergraduate teams were audiotaped in collaborative tasks (7514 talk turns). Discourse was coded for regulation processes and types (self and socially shared regulation), and analyzed with Epistemic Network Analysis and Process Mining. We find that teams who reported more frequent social exchange engaged in shared regulation together with planning and monitoring more frequently, while teams with less exchange engaged in long durations of collaboration. Furthermore, students in teams with more engaged regulation reported enhanced beliefs in group efficacy to solve collaborative tasks. The study illustrates the potential of applying quantitative approaches to analyzing rich discourse.

Studies have demonstrated that utilizing spacing (spreading out study sessions at regular intervals) and self-testing strategies are optimal for learning. While some applied work has examined the relationship between these strategies on general academic achievement, there is still a need to explore how both spacing and self-testing are related to course-level learning outcomes. The goal of this study was to examine whether utilizing spacing and self-testing strategies was related to final course grade. Participants were asked to report the study strategies they utilized during the course. We found no relationship between using spacing strategies and final course grade. However, self-testing was significantly related to higher final course grades. The relationship between self-testing and final course grade remained significant even after accounting for demographic differences and prior academic achievement. Our results suggest that self-testing strategies can enhance learning even in a highly structured course. © The Author(s) 2018.